Dialogue system using extended domain and natural language recognition method and computer-readable medium thereof

ABSTRACT

A dialogue system uses an extended domain in order to have a dialogue with a user using natural language. If a dialogue pattern actually input by the user is different from a dialogue pattern predicted by an expert, an extended domain generated in real time based on user input is used and an extended domain generated in advance is used to have a dialogue with the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of Korean Patent Application No.10-2010-0000271, filed on Jan. 4, 2010 in the Korean IntellectualProperty Office, the disclosure of which is incorporated herein byreference.

BACKGROUND

1. Field

Example embodiments relate to a dialogue system using an extendeddomain, which has a dialogue using a natural language used by a human,and a natural language recognition method and computer-readable mediumthereof.

2. Description of the Related Art

Language is the basic means of human communication. Natural languagerecognition in a robot or an Automatic Response System (ARS) serviceproviding apparatus communicating with a human is a recent major topic.

It is very difficult for a robot to completely understand all naturallanguage used by a human and to respond thereto. Since it is difficultto translate all natural language into meaningful language and to usethe meaningful language, the natural language is translated intomeaningful language only within a group (hereinafter, referred to as adomain) obtained by organizing meanings necessary for a use environmentand expressions corresponding thereto by an expert. For example, in thecase where an expert configures a domain including an input set “Whereis my cup?,” when a user actually inputs an input set “Where is mymug?,” because this input set is not present in the domain, the meaningthereof may not be understood. Since a dialogue pattern actually inputby a user may be different from a dialogue pattern predicted by anexpert, a natural language recognition rate of a dialogue systemdeteriorates.

SUMMARY

Therefore, it is an aspect of the example embodiments to provide adialogue system which has a dialogue with a user using an extendeddomain to increase a natural language recognition rate, and arecognition method and computer-readable medium thereof.

The foregoing and/or other aspects are achieved by providing a method ofrecognizing natural language in a dialogue system, the method includingparsing, by a processor, voice data of a user into syllables to generatean input set, understanding, by the processor, a user language using aninput set of an initial domain generated before dialogue with the userand an extended input set of an extended domain generated upon dialoguewith the user, generating, by the processor, a response sentence basedon a result of understanding the user language and generating, by theprocessor, user language based on the response sentence.

When the parsed input set is not included in the initial domain, theextended domain may be used.

The extended domain may be generated by generating the extended inputset using associated language data of a certain word configuring theparsed input set.

The associated language data may include at least one of a synonym, ahyponym and a hypernym associated with the certain word, and theextended input set may be generated by changing at least one word of theparsed input set to the associated language data.

The method may further include applying a weight to the extended inputset in order to restrict a size of the generated extended domain, andstopping the generation of the extended domain if the weight applied tothe extended input set is greater than a predefined value.

The weight may be differently applied depending on one of which wordsand how many words of the parsed input set are changed.

A weight applied to the extended input set generated by changing acertain word of the parsed input set to a hyponym may be greater than aweight applied to the extended input set generated by changing thecertain word to a synonym, and a weight applied to the extended inputset generated by changing the certain word to a hypernym may be greaterthan the weight applied to the extended input set generated by changingthe certain word to the hyponym.

The foregoing and/or other aspects are achieved by providing a method ofrecognizing natural language in a dialogue system, the method includingparsing, by a processor, voice data of a user into syllables to generatean input set, generating, by the processor, an extended domain includingan extended input set generated using an initial input set generated bypredicting meanings necessary for a use environment and expressionscorresponding thereto and associated language data of the initial inputset before dialogue with a user, and understanding user language usingthe extended input set of the extended domain, generating, by theprocessor, a response sentence based on a result of understanding theuser language and generating, by the processor, user language based onthe response sentence.

The foregoing and/or other aspects are achieved by providing a dialoguesystem using an extended domain, the dialogue system including alanguage parser to parse voice data of a user into syllables to generatean input set, a language understanding unit to understand user languageusing an input set of an initial domain generated before dialogue withthe user and an extended input set of an extended domain generated upondialogue with the user, a dialogue manager to generate a responsesentence based on a result of understanding the user language, and alanguage generator to generate user language based on the responsesentence.

The language understanding unit may further include an initial domaincomparator to determine whether the parsed input set is included in theinitial domain, and an extended domain comparator to determine whetherthe parsed input set is included in the extended domain, and, if theparsed input set is not included in the initial domain, the extendeddomain comparator may make a request for generation of the extendeddomain.

The language understanding unit may further include an associated wordbank having associated language data of a certain word configuring theparsed input set, an input set generator to generate the extended inputset using the associated language data, and an extended domain generatorto make a request for generation of the extended input set to the inputset generator according to the request for the generation of theextended domain.

The associated language data may include at least one of a synonym, ahyponym and a hypernym associated with the certain word, and the inputset generator may change at least one word of the parsed input set tothe associated language data.

The language understanding unit may further include a domain restrictorto restrict a size of the extended domain, and the domain restrictor mayapply a weight to the extended input set and make a request for stoppageof the generation of the extended input set to the input set generatorif the weight applied to the extended input set is greater than apredefined value.

The weight may be differently applied depending on one of which wordsand how many words of the parsed input set are changed.

The domain restrictor may apply, to the extended input set generated bychanging a certain word of the parsed input set to a hyponym, a weightgreater than a weight applied to the extended input set generated bychanging the certain word to a synonym, and apply to the extended inputset generated by changing the certain word to a hypernym a weightgreater than the weight applied to the extended input set generated bychanging the certain word to the hyponym.

The foregoing and/or other aspects are achieved by providing a dialoguesystem using an extended domain, the dialogue system including alanguage parser to parse voice data of a user into syllables to generatean input set, a language understanding unit to generate an extendeddomain including an extended input set generated using an initial inputset generated by predicting meanings necessary for a use environment andexpressions corresponding thereto and associated language data of theinitial input set before dialogue with a user, and to understand userlanguage using the extended input set of the extended domain, a dialoguemanager to generate a response sentence based on a result ofunderstanding the user language and a language generator to generateuser language based on the response sentence.

According to example embodiments, if a dialogue pattern actually inputby a user is different from a dialogue pattern predicted by an expert,an extended domain generated in real time based on a user input is usedand an extended domain generated in advance is used so as to have adialogue with the user. Accordingly, a natural language recognition rateof a dialogue system is improved.

BRIEF DESCRIPTION OF THE DRAWINGS

These and/or other aspects and advantages will become apparent and morereadily appreciated from the following description of the embodiments,taken in conjunction with the accompanying drawings of which:

FIG. 1 is a diagram showing the configuration of a dialogue system usingan extended domain according to example embodiment;

FIG. 2 is a diagram illustrating an input set present in an initialdomain according to example embodiments;

FIGS. 3A and 3B are tables illustrating associated language dataassociated with a certain word of an input set according to exampleembodiments;

FIGS. 4A to 4D are diagrams illustrating generation of an extended inputset to generate an extended domain according to example embodiments;

FIG. 5 is a table illustrating an extension type and a weight of anextended input set according to example embodiments;

FIG. 6 is a schematic diagram showing the range of an extended domainaccording to example embodiments;

FIG. 7 is a flowchart illustrating a natural language recognition methodof a dialogue system using an extended domain according to exampleembodiments;

FIG. 8 is a diagram showing the configuration of a dialogue system usingan extended domain according to example embodiments; and

FIG. 9 is a flowchart illustrating a natural language recognition methodof a dialogue system using an extended domain according to exampleembodiments.

DETAILED DESCRIPTION

Reference will now be made in detail to the embodiments, examples ofwhich are illustrated in the accompanying drawings, wherein likereference numerals refer to like elements throughout.

In a dialogue system using natural language, a dialogue with a user mayprogress by understanding text input using a keyboard or languagerecognized by voice recognition.

As shown in FIG. 1, a dialogue system 10 using an extended domainaccording to example embodiments may include a voice recognizer 100, alanguage parser 101, a language understanding unit 102, a dialoguemanager 111 and a language generator 112.

The voice recognizer 100 may convert user voice input through amicrophone into voice data and provide the voice data to the languageparser 101.

The language parser 101 may receive the voice data, parse the voice datainto syllables to generate an input set, and provide the parsed inputset to the language understanding unit 102.

User input is not limited to voice. A user may input text using akeyboard instead of the voice recognizer 100 and the language parser 101may generate and provide an input set.

The language understanding unit 102 may understand language input by theuser based on the parsed input set and provide the understood result tothe language manager 111. The language manager 111 may generate aresponse sentence to define a dialogue based on the understood result ofthe language, and provide the response sentence to the languagegenerator 112. The language generator 112 may generate languagecorresponding to the response sentence. Thereafter, the languagegenerated by the language generator 112 may be audibly output through aspeaker to have a dialogue with a person. Text may be displayedsimultaneously with or separately from a voice output, to have adialogue with the user.

When the language understanding unit 102 understands the language basedon the parsed input set, the input set may or may not be present in aninitial domain 104. The initial domain 104 has input sets generated byorganizing meanings necessary for a use environment and expressionscorresponding thereto. For example, an input set 20 shown in FIG. 2 maybe generated by an expert in advance.

A user dialogue pattern may be different from a dialogue patternpredicted by an expert. Accordingly, the parsed input set may not beincluded in the initial domain 104. In this case, an extended domain 110may be generated by input sets generated using an associated word bank108 of the language understanding unit 102, and the language input bythe user may be understood by an input set present in the extendeddomain 110.

The associated word bank 108 may include a database establishedaccording to associated language data (a synonym, a hyponym and ahypernym) of a certain word. The synonym may have the same meaning as acertain word and may replace the certain word. The hyponym may notreplace a certain word but may include subordinate-concept words of thecertain word, and may be used to directly infer the certain word. Thehypernym may include superordinate-concept words of a certain word, andmay be used to indirectly infer the certain word.

For example, in associated language data 30 of a word “Cup”, as shown inFIG. 3A, a word registered as a synonym of the word “cup” is notpresent, words “coffee cup” and “teacup” are registered as a hyponym of“cup”, and words “dishware”, “tableware” and “container” are registeredas a hypernym of “cup.” As another example, in associated language dataof a word “mother”, as shown in FIG. 3B, the words “female parent” areregistered as a synonym of the word “Mother,” words “mama” and “mom” areregistered as a hyponym of the word “Mother,” and a word registered as ahypernym of the word “Mother” is not present.

The input set generator 109 may receive the associated language datafrom the associated word bank 108 according to a request of an extendeddomain generator 106 and generate input sets necessary for domainextension. The extended domain 110 may be generated by the generatedinput sets.

A method of generating input sets by the input set generator 109 willnow be described.

As shown in FIG. 4A, two extended input sets 21 and 22 may be generatedby changing a word “mother” of an input set 20 to the synonym “femaleparent” and the hyponym “mama.” In addition, as shown in FIG. 4B, twoextended input sets 23 and 24 may be generated by changing a word “cup”of the input set 20 to the hyponyms “coffee cup” and “teacup.” As shownin FIG. 4C, two extended input sets 25 and 26 may be generated bychanging a word “cup” of the input set 20 to the hypernyms “dishware”and “container.”

In addition, at least two words included in the input set 20 may bechanged. As shown in FIG. 4D, two extended input sets 27 and 28 may begenerated by changing the word “cup” of the input set 20 to the hyponym“coffee cup” and changing the word “mother” to the synonym “femaleparent” and the hyponym “mama.”

Since an excessive number of input sets may be generated by the inputset generator 109 generating input sets using the associated languagedata provided by the associated word bank 108, adequate restriction isnecessary. A domain restrictor 107 may restrict the size of the extendeddomain 110 so that the number of input sets present in the extendeddomain 110 is not excessively increased.

The domain restrictor 107 may define extension types depending on whichwords or how many words of the input set generated by the user input arechanged and apply weights according to the extension types, as shown inFIG. 5. At this time, the degree of change of the extended input setgenerated from the input set may be checked by the applied weight.

If a changed portion of the extended input set has a meaning similar toa meaning thereof before change, a small weight may be applied and if achanged portion of the extended input set has a different meaning, alarge weight may be applied. For example, a weight “1” may be applied toan extended input set obtained by changing a word to a synonym, a weight“2” may be applied to an extended input set obtained by changing a wordto a hyponym, and a weight “3” may be applied to an extended input setobtained by changing a word to a hypernym.

As shown in FIG. 5, respective weights may be applied to the extendedinput sets 21, 22, 23, 24, 25, 26, 27 and 28 generated from the inputset 20 of the initial domain 104 according to the extension types. If aweight sum is large, the number of changed portions may be relativelylarge and, if the weight sum is small, the number of changed portionsmay be relatively small.

Since the weights are applied to the extended input sets, the appliedweights and a predefined value to restrict the domain may be compared torestrict the size of the extended domain 110. An input set having aweight sum less than the predefined value may be included in theextended domain 110 and an input set having a weight sum greater thanthe predefined value may not be included in the extended domain 110,thereby restricting the size of the extended domain 110. Thus, theextended domain 110 may include only input sets each having a weight sumless than the predefined value. For example, if the predefined value isset to 3 in order to restrict the size of the extended domain 110, onlyseven input sets 21, 22, 23, 24, 25, 26 and 27 may be included in theextended domain 110, excluding one input set 28 having a weight sum of4. As another example, if the predefined value is set to 6 in order torestrict the size of the extended domain 110, all eight input sets 21,22, 23, 24, 25, 26, 27 and 28 each having a weight sum less than thepredefined value may be included in the extended domain 110.

The range ED of the extended domain 110 restricted by the weights may beshown schematically in FIG. 6. The range ED of the extended domain 110may be obtained in the case where the predefined value is set to 6. Acertain word K may be consecutively changed to synonyms having a weightof 1 to perform 6-step extension. The consecutive change may indicatethat the changed synonym is changed to another synonym again. Inaddition, a certain word may be consecutively changed to hyponyms toperform 3-step extension or may be consecutively changed to hypernyms toperform 2-step extension. If the weight sum does not exceed 6, thesynonym, the hyponym and the hypernym may be mixed to perform extension.

The range ED of the extended domain 110 may be wider in a first synonymdirection A1 and a second synonym direction A2 to which a relativelysmall weight is applied than in a first hyponym direction B1 and a firsthypernym direction C1. The range ED of the extended domain 110 generatedin consideration of weights may be wider in the synonym direction than adomain D extending to a constant distance from a certain word K in allassociated word (synonym, hyponym and hypernym) directions. Input setswhich are not included in the domain D may be included in a hatchedportion H.

Since the range ED of the extended domain 110 extends widely in thesynonym direction, an input set more similar to an input pattern of theuser in terms of meaning may be included although an input dialoguepattern of the user deviates from a predicted dialogue pattern. Inaddition, the size of the domain may decrease.

Hereinafter, the operation of a dialogue system using an extended domainaccording to the example embodiments will be described.

Referring to FIG. 7, a user who wants to have a dialogue may input avoice signal (201). Then, the voice recognizer 100 may convert the inputvoice signal into voice data and provide the voice data to the languageparser 101. The signal input by the user may not be limited to the voicesignal. Although not specially described in the example embodiments, theuser who wants to have a dialogue may input text using a keyboard toprovide the text to the language parser 101.

If the voice data is received (Yes in 203), voice recognition may beperformed using the voice data provided by the language parser 101, thevoice data may be parsed into syllables to generate an input set, andprovide the input set to an initial domain comparator 103 (205 and 207).

The initial domain comparator 103 may determine whether the parsed inputset is included in the initial domain 104 (209). If the parsed input setis included in the initial domain 104 (Yes in 209), then the languagemay be understood based on the input set and the understood result maybe provided to the dialogue manager 111. Then, the dialogue manager 111may generate and provide the response sentence to define the dialogue tothe language generator 112, and the language generator 112 may generatelanguage used to have a dialogue with the user according to the responsesentence (219). Thereafter, the language generated by the languagegenerator 112 may be audibly output through a speaker, thereby providinga dialogue with the user (221). The language generated by the languagegenerator 112 may be displayed as text simultaneously with or separatelyfrom a voice output, thereby providing a dialogue with the user.

If the parsed input set is not included in the initial domain 104 (No in209), an extended domain comparator 105 may provide the parsed input setto an extended domain generator 106 to make a request for domainextension. Then, the extended domain generator 106 may make a requestfor generation of an input set to generate an extended domain to theinput set generator 109. The input set generator 109 may generateextended input sets based on the synonym, the hyponym and the hypernymreceived from the associated word bank 108 with respect to wordsconfiguring the parsed input set. As shown in FIGS. 4A to 4D, theextended input sets may be generated using the synonym, the hyponym andthe hypernym and the extended domain 110 may be generated using thegenerated extended input sets (211).

The domain restrictor 107 may apply weights to the generated extendedinput sets and provide information about the weights to the extendeddomain generator 106. The information about the weights may includeweights applied according to extension types defined depending on whichwords or how many words of the input set are changed. The degree ofchange of the input set may be checked by the weights applied to theextended input sets. If a changed portion of the extended input set hasa meaning similar to a meaning thereof before change, a small weight maybe applied and if a changed portion of the extended input set has adifferent meaning from a previous meaning, a large weight may be applied(213).

The extended domain generator 106 may determine whether the size of theextended domain 110 needs to be restricted according to the informationabout the weights received from the domain restrictor 107. If the sum ofweights applied to the input set generated by the input set generator109 is less than the predefined value (No in 215), the domain maycontinue to be extended. If the sum of weight applied to the input setgenerated by the input set generator 109 is greater than the predefinedvalue, it is recognized that the size of the extended domain 110 mayneed to be restricted (Yes in 215) to make a request for stoppage of thegeneration of the input set to the input set generator 109. Then, theinput set generator 109 may stop the generation of the input set.

After generating the extended domain 110, the extended domain comparator105 may determine whether the parsed input set is included in theextended domain 110 (217). If the parsed input set is included in theextended domain 110 (Yes in 217), the extended domain comparator 105 mayunderstand the language based on the input set and provide theunderstood result of the language to the dialogue manager 111. Then, thedialogue manager 111 may generate a response sentence to define adialogue and provide the response sentence to the language generator112, and the language generator 112 may generate language to have adialogue with the user according to the response sentence (219). Thelanguage generated by the language generator 112 may be audibly outputthrough the speaker (221). The language generated by the languagegenerator 112 may be displayed as text simultaneously with or separatelyfrom voice output, thereby providing a dialogue with the user.

If the parsed input set is not included in the extended domain 110 (Noin 217), the dialogue system 10 may ignore the user input, finish theprocess, and wait for user voice input.

Although an extended domain may be generated with respect to user voiceinput in real time to have a dialogue with a user in the exampleembodiments, an extended domain may be generated in advance to have adialogue with a user based on the extended domain as in the followingexample embodiments.

As shown in FIG. 8, a dialogue system 11 using an extended domainaccording to example embodiments may include a voice recognizer 100,language parser 101, language understanding unit 122, dialogue manager111, and language generator 112. The components other than the languageunderstanding unit 122 are substantially equal to those of theabove-described example embodiments and are denoted by the samereference numerals.

The language understanding unit 122 may include an extended domaincomparator 123 and an extended domain 124.

The extended domain 124 may include an initial input set generated bypredicting meanings necessary for a use environment and expressionscorresponding thereto and an extended input set generated usingassociated language data (synonym, hyponym and hypernym) of the initialinput set. The extended domain 124 may be generated in advance. Then,when having a dialogue with a user, the extended domain comparator 123may understand language using input sets included in the extended domain124. Thereafter, language to define a dialogue may be generated based onthe understanding of the language and audibly output, thereby providinga dialogue with the user.

Referring to FIG. 9, the extended domain 124 may be generated in advance(301) and a user may input a voice signal (303). The voice recognizer100 may convert the input voice signal into voice data and provide thevoice data to the language parser 101. The user may input text through akeyboard to provide the text to the language parser 101.

If the voice data is received (Yes in 305), the language parser 101 mayperform voice recognition based on the voice data, parse the voice datato generate an input set, and provide the input set to the extendeddomain comparator 123 (307 and 309).

The extended domain comparator 123 may determine whether the parsedinput set is included in the extended domain 124 (311).

If the parsed input set is included in the extended domain 124 (Yes in311), language may be understood based on the input set and theunderstood result of the language may be provided to the dialoguemanager 111. Then, the dialogue manager 111 may generate a responsesentence to define a dialogue and provide the response sentence to thelanguage generator 112, and the language generator 112 may generate userlanguage according to the response sentence (313). The user languagegenerated by the language generator 112 may be audibly output through aspeaker (315). The language generated by the language generator 112 maybe displayed as text simultaneously with or separately from voiceoutput, thereby providing a dialogue with the user.

The above-described embodiments may be recorded in non-transitorycomputer-readable media including program instructions to implementvarious operations embodied by a computer. The media may also include,alone or in combination with the program instructions, data files, datastructures, and the like. Examples of computer-readable media(computer-readable storage devices) include magnetic media such as harddisks, floppy disks, and magnetic tape; optical media such as CD ROMdisks and DVDs; magneto-optical media such as optical disks; andhardware devices that are specially configured to store and performprogram instructions, such as read-only memory (ROM), random accessmemory (RAM), flash memory, and the like. The computer-readable mediamay be a plurality of computer-readable storage devices in a distributednetwork, so that the program instructions are stored in the plurality ofcomputer-readable storage devices and executed in a distributed fashion.The program instructions may be executed by one or more processors orprocessing devices. The computer-readable media may also be embodied inat least one application specific integrated circuit (ASIC) or FieldProgrammable Gate Array (FPGA). Examples of program instructions includeboth machine code, such as produced by a compiler, and files containinghigher level code that may be executed by the computer using aninterpreter. The described hardware devices may be configured to act asone or more software modules in order to perform the operations of theabove-described exemplary embodiments, or vice versa.

Although embodiments have been shown and described, it should beappreciated by those skilled in the art that changes may be made inthese embodiments without departing from the principles and spirit ofthe disclosure, the scope of which is defined in the claims and theirequivalents.

What is claimed is:
 1. A method of recognizing natural language in adialogue system, the method comprising: parsing, by a processor, voicedata of a user into syllables to generate an input set; andunderstanding, by the processor, user language using an input set of aninitial domain generated before dialogue with the user and an extendedinput set of an extended domain generated upon dialogue with the user.2. The method according to claim 1, wherein when the parsed input set isnot included in the initial domain, the extended domain is used.
 3. Themethod according to claim 1, wherein the extended domain is generated bygenerating the extended input set using associated language data of acertain word configuring the parsed input set.
 4. The method accordingto claim 3, wherein: the associated language data includes at least oneof a synonym, a hyponym and a hypernym associated with the certain word,and the extended input set is generated by changing at least one word ofthe parsed input set to the associated language data.
 5. The methodaccording to claim 4, further comprising: applying a weight to theextended input set in order to restrict a size of the generated extendeddomain, and stopping the generation of the extended domain if the weightapplied to the extended input set is greater than a predefined value. 6.The method according to claim 5, wherein the weight is differentlyapplied depending on one of which words and how many words of the parsedinput set are changed.
 7. The method according to claim 6, wherein aweight applied to the extended input set generated by changing a certainword of the parsed input set to a hyponym is greater than a weightapplied to the extended input set generated by changing the certain wordto a synonym, and a weight applied to the extended input set generatedby changing the certain word to a hypernym is greater than the weightapplied to the extended input set generated by changing the certain wordto the hyponym.
 8. A method of recognizing natural language in adialogue system, the method comprising: parsing, by a processor, voicedata of a user into syllables to generate an input set; and generating,by the processor, an extended domain including an extended input setgenerated using an initial input set generated by predicting meaningsnecessary for a use environment and expressions corresponding theretoand associated language data of the initial input set before dialoguewith a user, and understanding user language using the extended inputset of the extended domain.
 9. A dialogue system using an extendeddomain, the dialogue system comprising: a processor to control one ormore processor-executable units; a language parser to parse voice dataof a user into syllables to generate an input set; and a languageunderstanding unit to understand user language using an input set of aninitial domain generated before dialogue with the user and an extendedinput set of an extended domain generated upon dialogue with the user.10. The dialogue system according to claim 9, wherein: the languageunderstanding unit further includes: an initial domain comparator todetermine whether the parsed input set is included in the initialdomain; and an extended domain comparator to determine whether theparsed input set is included in the extended domain, wherein, if theparsed input set is not included in the initial domain, the extendeddomain comparator makes a request for generation of the extended domain.11. The dialogue system according to claim 10, wherein the languageunderstanding unit further includes: an associated word bank havingassociated language data of a certain word configuring the parsed inputset; an input set generator to generate the extended input set using theassociated language data; and an extended domain generator to make arequest for generation of the extended input set to the input setgenerator according to the request for the generation of the extendeddomain.
 12. The dialogue system according to claim 11, wherein theassociated language data includes at least one of a synonym, a hyponymand a hypernym associated with the certain word, and the input setgenerator changes at least one word of the parsed input set to theassociated language data.
 13. The dialogue system according to claim 11,wherein: the language understanding unit further includes a domainrestrictor to restrict a size of the extended domain, and the domainrestrictor applies a weight to the extended input set and makes arequest for stoppage of the generation of the extended input set to theinput set generator if the weight applied to the extended input set isgreater than a predefined value.
 14. The dialogue system according toclaim 13, wherein the weight is differently applied depending on one ofwhich words and how many words of the parsed input set are changed. 15.The dialogue system according to claim 13, wherein the domain restrictorapplies to the extended input set generated by changing a certain wordof the parsed input set to a hyponym a weight greater than a weightapplied to the extended input set generated by changing the certain wordto a synonym, and applies to an extended input set generated by changingthe certain word to a hypernym a weight greater than the weight appliedto the extended input set generated by changing the certain word to thehyponym.
 16. A dialogue system using an extended domain, the dialoguesystem comprising: a processor to control one or moreprocessor-executable units; a language parser to parse voice data of auser into syllables to generate an input set; and a languageunderstanding unit to generate an extended domain including an extendedinput set generated using an initial input set generated by predictingmeanings necessary for a use environment and expressions correspondingthereto and associated language data of the initial input set beforedialogue with a user, and to understand user language using the extendedinput set of the extended domain.
 17. At least one non-transitorycomputer readable medium comprising computer readable instructions thatcontrol at least one processor to implement the method of claim
 1. 18.At least one non-transitory computer readable medium comprising computerreadable instructions that control at least one processor to implementthe method of claim
 8. 19. A dialogue system using an extended domain,the dialogue system comprising: a processor to control one or moreprocessor-executable units; a dialogue manager to generate a responsesentence based on a result of understanding a user language; and alanguage generator to generate user language according to the responsesentence by using an initial domain generated before dialogue with theuser and an extended domain.
 20. A dialogue system using an extendeddomain, the dialogue system comprising: a processor to control one ormore processor-executable units; a language parser to parse voice dataof a user into syllables to generate an input set; a languageunderstanding unit to understand user language using an input set of aninitial domain generated before dialogue with the user and an extendedinput set of an extended domain generated upon dialogue with the user; adialogue manager to generate a response sentence based on a result ofunderstanding the user language; and a language generator to generateuser language according to the response sentence by using the initialdomain generated before dialogue with the user and the extended domain.